Nowadays, intelligent systems or automated systems are faster and dependable efficient alternatives to human resources for automating tasks. Intelligent Systems are requisite for companies that intend to thrive in Information Technology field. The intelligent systems are dependent on knowledge base, including, but are not limited to, user manuals, troubleshooting guides, instruction manuals which consist of instructions for automating the tasks. But the documents incorporate numerous lines of natural language texts that relay sets of instructions, and such resources are not utilizable in that very format by the intelligent system to automate the tasks. Furthermore, huge amount of human effort is required for reading, comprehending, grasping the natural language texts and then actuating the same on the intelligent system.
So much of effort goes in understanding small set of instructions, which if well formulated, can be easily interpreted and simulated by the intelligent system. Consider an exemplary natural language instruction taken from a manual in the knowledge base which is: “Remove the battery from the battery compartment. Press and hold the Power button to drain residual electrical charge from the capacitors that protect the memory”. This instruction may be condensed and translated into two simple instruction sets as given below:                1. Remove->Battery        2. Press and Hold->Power button        
These instructions are easy to interpret both for a human as well as the intelligent system. But, it would be highly beneficial to have a system that converts and translates the umpteen number of natural language instructions in the user manuals, troubleshooting guides and similar documents to instruction sets that can be easily understood and executed by the intelligent system.
Currently, existing systems that address the above mentioned problems are majorly based on identifying part-of-speech tags of the natural language texts. Since part-of-speech extraction is not accurate, these systems do not scale above the accuracy of the Part-Of-Speech taggers. Besides, these systems are domain dependent and do not fare well across domains as they do not learn domain specific features automatically to interpret the instructions.